A Competitor of the Kolmogorov--Smirnov Test for the Goodness-of-fit Problem

Claudio Giovanni Borroni, Paola Maddalena Chiodini

Abstract

The classical goodness-of-fit problem, in the case of a null continuous and completely specified distribution, is faced by a new version of the Girone--Cifarelli test (see Girone, 1964; Cifarelli, 1974 \& 1975). This latter test was introduced for the two-sample problem and showed a substantial gain of power over other common tests based on the empirical distribution function, notably over the Kolmogorov--Smirnov test. First, the problem of the re-definition of the Girone--Cifarelli test-statistic is considered, by reviewing the literature on the subject. A classical remark by Anderson (1962) is shown to be useful to choose the integrating function in the newly defined test-statistic. The sample properties of such a test-statistic are then studied. A table of critical values is obtained by simulation; moreover, the asymptotic null distribution is considered and its accuracy as an approximation of the finite distribution is discussed. Finally, a simulation study, considering a wide set of distributions mostly used in applications, is conducted to compare the proposed test with its classical competitors. The study gives some indications to locate such situations where the Girone-Cifarelli test performs at its best, notably over the Kolmogorov--Smirnov test.

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